Utility companies are obligated to create demand projections and maintain a regulated reserve margin above it. The capacity that is above or below that margin can be bought or sold in the energy markets.
Furthermore, there is a growing tendency towards unbundling the power system as different sectors of the industry (generation, transmission, and distribution) are faced with increasing demand on planning management and operations of the networks. The operation and planning of a power utility company requires an adequate model for power load forecasting. This load forecasting plays a key role in helping a utility to make important decisions on power, load switching, voltage control, network reconfiguration, and infrastructure development.
Methodologies of load forecasts can be divided into various categories which include short-term forecasts, medium-term forecasts, and long-term forecasts. For example, short-term forecasting gives a forecast of load about one hour ahead of time. Such a forecast may assist in making decisions aimed at preventing an imbalance in the power generation and load demand, which would lead to greater network reliability and power quality.
Many methods have been used for load forecasting. These include statistical methods such as regression and similar-day approach, fuzzy logic, expert systems, support vector machines, econometric models, and end-use models.
New power forecasting models have been introduced such as artificial intelligence (AI), artificial neural network (ANN), and support vector machines (SVM).
An ANN is trained on input data as well as the associated target values. The trained network can then make predictions based on the relationships learned during training. Generally, ANN refers to a class of models inspired by the biological nervous system. The models are composed of many computing elements, usually denoted neurons; each neuron has a number of inputs and one output. It also has a set of nodes called synapses that connect to the inputs, output, or other neurons.
A linear combiner is used to produce a single value from all the inputs. The single value is the weighted sum of the inputs from which the threshold value associated with the neurons is subtracted to compose the activation of the neuron. The activation signal is passed through an activation function to produce the output of the neuron. The chosen activation function is normally a non-linear function (for example, a sigmoid function), a feature that allows the ANN to represent more complex problems.
Most ANN models focus in connection with short-term forecasting use multi-layer perceptron (MLP) networks. The attraction of MLP can be explained by the ability of the network to learn complex relationships between input and output patterns, which would be difficult to model with conventional methods. Inputs to the networks are generally present and past load values. The network is trained using actual load data from the past.
Within the power demand forecasting context, ANN uses data such as total regional demand for energy, weather, daylight hours, and large community events to project the short terms electricity demand of a given region. The mean error for this type of forecast is popularly reported as 1.5%, which may cost a utility millions of dollars in losses annually
There are significant other drawbacks to the ANN and like systems. One of the most salient of these is the ongoing requirement to have nothing impacting on the network which would lead to a loss of its generalizing capability.
There are a number of short-term load forecasting algorithms for utility applications and energy trading, such as ANNSTLF by Electric Power Research Institute (EPRI), and NOSTRADAMUS by Ventyx. These use ANN to model the regional load demand, primarily using load history and weather as inputs.
Moreover, there has been research in the area of power prediction and energy optimization using microgrids, or small, localized groups of energy generators/storers. Microgrid is defined as a system as follows:
“—designed, built, and controlled by “customers” based on internal requirements
—subject to the technical, economic, and regulatory opportunities and constraints faced
—a cluster of small (e.g. <500 kW) sources, storage systems, and loads which presents itself to the grid as a legitimate entity, i.e. as a good citizen interconnected with the familiar wider power system, or macrogrid, but can island from it.
The Micro Grid concept assumes a cluster of loads, micro-sources and storage operating as a single system . . . . ”1 1 Presented to the grid as a single controllable unit (impacts system reliability Microgrids and the Macrogrid Presentation to the California Public Utilities Commission 20 Feb. 2001 By Abbas Akhil, Chris Marnay, & Bob Lasseter Sandia National Laboratory, Berkeley Lab, and University of Wisconsin, Madison Consortium for Electric Reliability Technology Solutions (available publicly online at www.pserc.wisc.edu/documents/general...by.../certs_cpuc.ppt)
At this microgrid type level, monitoring and assessment of individual users and their loads is undertaken and is feasible. This type of system cannot be reliably or practically used to forecast within a macrogrid.
From the perspective of the consumer, as opposed to utility companies, there are some overlapping but also different concerns in regards to power usage. With the advent of “smart grid” technologies, also called “smart home”, “smart meter”, or “home area network” (HAN) technologies, optimized demand reductions became possible at the end use or appliance level. Smart grid technologies provided the ability to capture real-time or near-real-time end-use data and enabled two-way communication. Smart grid technologies currently exist for at least some percentage of a utility's customer base and applications are growing throughout North America. From a consumer perspective, smart metering offers a number of potential benefits to householders. These include a) An end to estimated bills, which are a major source of complaints for many customers b) A tool to help consumers better manage their energy use—smart meters with a display can provide up to date information on gas and electricity consumption in the currency of that country and in doing so help people to better manage their energy use and reduce their energy bills and carbon emissions
Electricity pricing usually peaks at certain predictable times of the day and the season. In particular, if generation is constrained, prices can rise from other jurisdictions or more costly generation is brought online. It is believed that billing customers by time of day will encourage consumers to adjust their consumption habits to be more responsive to market prices. Regulatory and market design agencies hope these “price signals” will delay the construction of additional generation or at least the purchase of energy from higher priced sources, thereby controlling the steady and rapid increase of electricity prices
Using smart grid technologies, a system operator can optimally and dynamically dispatch on and off signaling to specific appliances at a customer location given the observed and forecast loads of other appliances on a circuit or system.
It is an object of the present invention to obviate or mitigate the above disadvantages and to provide solutions for modeling and forecasting in the provision of power resources.